Group 11: Analysis of Prostate Cancer Data

Introduction

Prostate cancer, a disease that significantly impacts the lives of men, requires a thoughtful exploration to refine diagnostic approaches and treatment regime Understanding the complexities of this condition is crucial, emphasizing the importance of applying advanced data analysis techniques to patient data. Extracting meaningful insights from extensive datasets not only deepens our understanding of prostate cancer but also equips healthcare professionals with valuable information to personalise patient-by-patient Our goal is to delve into the connections, patterns, and predictive models associated with prostate cancer and patient outcomes, ultimately enhancing our ability to provide more personalized and effective care for individuals facing this challenging diagnosis.

Materials and Methods

Our analysis exploits data from a randomised clinical trial by Byar & Greene that compares treatment of patients with prostate cancer in stages 3 and 4. Treatment consisted of different doses of diethylstilbestrol (DES). Data are publicly available in : https://hbiostat.org/data/repo/prostate.xls The initial dataset contains information related to 502 observations of patients with prostate cancer across 18 variables. These variables encompass diverse information including patient demographics, medical history, treatment received, and health status. The raw data were loaded + augmented + described + modelled. and the process of arriving at results is done in a reproducible manner. For instance we separate “rx” into three columns; “Treatment regime”, “mg” and “Drug”

Data exploration

Data exploration

Results: Logistic regression modelling

Results: Principal Component Analysis (PCA)

There were 3 main steps to this PCA, outlined below: 1. Looking at the data in PC coordinates. 2. Looking at the rotation matrix. 3. Looking at the variance explained by each PC.

Discussion